This paper proposes a novel data preprocessing method that converts numeric data into
representative graphs (polygons) expressing all of the relationships between data variables …

This study presents the implementation of two discrete robust approaches to Non-linear
Model Predictive Control (NMPC), multi-scenario NMPC (MSc-NMPC) and multi-stage …

The performance evaluation of the process industry, which has been a popular topic
nowadays, can not only find the weakness and verify the resilience and reliability of the …

A stochastic real time optimization (SRTO) which has an efficient result has been
implemented on the Tennessee Eastman (TE) challenging problem. In this article a novel …

Modifier adaptation (MA) methods are iterative model-based real-time optimization (RTO)
methods with the proven ability to reach, upon converge, the unknown optimal steady-state …

This study presents a novel algorithm for constructing a probabilistic model based on
historical operation data and performing dynamic optimization for plant-wide control …

Nonlinear characteristic fault detection and diagnosis method based on higher-order
statistical (HOS) is an effective data-driven method, but the calculation costs much for a large …

In this work, the authors describe a concept called Virtual Mass Balance and its applicability
to oil and gas fields. This study relies on data modelling and data engineering to enrich …

Any industrial system goes along with objectives to be met (eg economic performance),
disturbances to handle (eg market fluctuations, catalyst decay, unexpected variations in …

Conventional real-time optimization (RTO) algorithms provide the steady-state set points at
which the process would operate economically. However, the process may suffer from …